Create app.py

app.py

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+import re, numpy as np, torch, gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from scipy.ndimage import uniform_filter1d
+
+MODEL = "facebook/xglm-564M"   # upgrade to xglm-2.9B if you get a GPU Space
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+tok = AutoTokenizer.from_pretrained(MODEL)
+lm  = AutoModelForCausalLM.from_pretrained(MODEL).to(DEVICE).eval()
+
+def split_words(text: str):
+    return re.findall(r"\w+|[^\w\s]", text, flags=re.UNICODE)
+
+@torch.inference_mode()
+def word_surprisal(text: str):
+    enc = tok(text, return_tensors="pt", return_offsets_mapping=True)
+    ids = enc["input_ids"].to(DEVICE)
+    offs = enc["offset_mapping"][0].tolist()
+
+    out = lm(ids)
+    logits = out.logits[:, :-1, :]
+    targets = ids[:, 1:]
+    logp = torch.log_softmax(logits, dim=-1)
+    ll = logp.gather(-1, targets.unsqueeze(-1)).squeeze(-1)   # [1, T-1]
+    nll = (-ll).squeeze(0).cpu().numpy()
+    sub_offs = offs[1:]
+
+    words = split_words(text)
+    # char spans for words
+    spans = []
+    pos = 0
+    for w in words:
+        start = text.find(w, pos)
+        spans.append((start, start+len(w)))
+        pos = start+len(w)
+
+    w_scores = np.zeros(len(words), dtype=float)
+    for s,(a,b) in zip(nll, sub_offs):
+        if a==b: continue
+        for i,(ws,we) in enumerate(spans):
+            if a>=ws and b<=we:
+                w_scores[i] += float(s); break
+    return words, w_scores
+
+def robust_threshold(scores, k=2.5):
+    if len(scores)==0:
+        return 1e9
+    med = float(np.median(scores))
+    mad = float(np.median(np.abs(scores - med))) + 1e-8
+    return med + k*mad
+
+def infer(text, k=2.5, smooth=2):
+    words, scores = word_surprisal(text)
+    if smooth and smooth>1:
+        scores = uniform_filter1d(scores, size=smooth, mode="nearest")
+    thr = robust_threshold(scores, k=k)
+    flagged = [i for i,s in enumerate(scores) if s>=thr]
+
+    out = []
+    for i,w in enumerate(words):
+        out.append(f"<mark style='background:#ffb3b3'>{w}</mark>" if i in flagged else w)
+    md = " ".join(out).replace(" ."," .").replace(" ,"," ,")
+    table = [(w, float(s)) for w,s in zip(words, scores)]
+    return md, table
+
+demo = gr.Interface(
+    fn=infer,
+    inputs=[
+        gr.Textbox(lines=3, label="Cümle girin"),
+        gr.Slider(1.5, 4.0, value=2.5, step=0.1, label="Threshold (median + k*MAD)"),
+        gr.Slider(1, 7, value=2, step=1, label="Smoothing window (words)")
+    ],
+    outputs=[
+        gr.HTML(label="Highlighted"),
+        gr.Dataframe(headers=["Word","Score"], label="Word surprisal (lower=more normal)")]
+    ,
+    title="Odd-Word Detector — XGLM (autoregressive)",
+    description="Autoregressive next-token likelihood per word; flags unusually unlikely words."
+)
+
+if __name__ == "__main__":
+    demo.launch()